Mathematics

Die Numerische Mathematik ist einer der Grundpfeiler des Mathematik-, Ingenieur-, Physik- und Informatikstudiums. Dieses zweibändige Lehrbuch ist für Einführungsvorlesungen konzipiert und legt eine solide Basis für weiterführende Lerneinheiten. Der Text ist aus Vorlesungsmanuskripten hervorgegangen, die der Verfasser seit etwa 30 Jahren für seine Grundvorlesungen auf dem Gebiet der Numerischen Mathematik und des Wissenschaftlichen Rechnens an der Friedrich-Schiller-Universität Jena verwendet.

Das Buch deckt den gesamten Bereich der Numerischen Mathematik von den klassischen Techniken wie Gaußscher Algorithmus und Newtonsches Verfahren bis hin zu modernen Algorithmen wie kubische Spline-Interpolation, Kleinste-Quadrate-Approximation mittels Householder- und Givens-Transformationen sowie Deflationstechniken ab. Die Verfahren werden mathematisch exakt beschrieben, in MATLAB-Codes implementiert und anhand von Beispielen demonstriert. Die MATLAB-Codes sind auf der Webseite des Verlages zum Download bereitgestellt, so dass der Leser seine eigenen Experimente mit den numerischen Verfahren durchführen kann.

Durch seinen didaktischen Aufbau und die zahlreichen anschaulichen Beispiele und Übungsaufgaben eignet sich dieses Buch hervorragend als vorlesungsbegleitende Lektüre und als Grundlage für ein erfolgreiches Selbststudium. Gleichzeitig kann es von Mathematikern, Naturwissenschaftlern und Ingenieuren als profundes Nachschlagewerk herangezogen werden.

Mit der 4. Auflage wurde das umfangreiche Standardwerk der Numerischen Mathematik so in zwei Bände aufgeteilt, dass diese relativ unabhängig voneinander gelesen werden können. An vielen Stellen wurde der Text überarbeitet und ergänzt. Das betrifft insbesondere diejenigen Abschnitte, die für Lehrerstudenten relevant sind sowie die Implementierung der numerischen Verfahren in der Programmiersprache MATLAB.

Die Numerische Mathematik ist einer der Grundpfeiler des Mathematik-, Ingenieur-, Physik- und Informatikstudiums. Dieses zweibändige Lehrbuch ist für Einführungsvorlesungen konzipiert und legt eine solide Basis für weiterführende Lerneinheiten. Der Text ist aus Vorlesungsmanuskripten hervorgegangen, die der Verfasser seit etwa 30 Jahren für seine Grundvorlesungen auf dem Gebiet der Numerischen Mathematik und des Wissenschaftlichen Rechnens an der Friedrich-Schiller-Universität Jena verwendet.

Das Buch deckt den gesamten Bereich der Numerischen Mathematik von den klassischen Techniken wie Gaußscher Algorithmus und Newtonsches Verfahren bis hin zu modernen Algorithmen wie kubische Spline-Interpolation, Kleinste-Quadrate-Approximation mittels Householder- und Givens-Transformationen sowie Deflationstechniken ab. Die Verfahren werden mathematisch exakt beschrieben, in MATLAB-Codes implementiert und anhand von Beispielen demonstriert. Die MATLAB-Codes sind auf der Webseite des Verlages zum Download bereitgestellt, so dass der Leser seine eigenen Experimente mit den numerischen Verfahren durchführen kann.

Durch seinen didaktischen Aufbau und die zahlreichen anschaulichen Beispiele und Übungsaufgaben eignet sich dieses Buch hervorragend als vorlesungsbegleitende Lektüre und als Grundlage für ein erfolgreiches Selbststudium. Gleichzeitig kann es von Mathematikern, Naturwissenschaftlern und Ingenieuren als profundes Nachschlagewerk herangezogen werden.

Mit der 4. Auflage wurde das umfangreiche Standardwerk der Numerischen Mathematik so in zwei Bände aufgeteilt, dass diese relativ unabhängig voneinander gelesen werden können. An vielen Stellen wurde der Text überarbeitet und ergänzt. Das betrifft insbesondere diejenigen Abschnitte, die für Lehrerstudenten relevant sind sowie die Implementierung der numerischen Verfahren in der Programmiersprache MATLAB.

Die Numerische Mathematik ist einer der Grundpfeiler des Mathematik-, Ingenieur-, Physik- und Informatikstudiums. Dieses zweibändige Lehrbuch ist für Einführungsvorlesungen konzipiert und legt eine solide Basis für weiterführende Lerneinheiten. Der Text ist aus Vorlesungsmanuskripten hervorgegangen, die der Verfasser seit etwa 30 Jahren für seine Grundvorlesungen auf dem Gebiet der Numerischen Mathematik und des Wissenschaftlichen Rechnens an der Friedrich-Schiller-Universität Jena verwendet.

Das Buch deckt den gesamten Bereich der Numerischen Mathematik von den klassischen Techniken wie Gaußscher Algorithmus und Newtonsches Verfahren bis hin zu modernen Algorithmen wie kubische Spline-Interpolation, Kleinste-Quadrate-Approximation mittels Householder- und Givens-Transformationen sowie Deflationstechniken ab. Die Verfahren werden mathematisch exakt beschrieben, in MATLAB-Codes implementiert und anhand von Beispielen demonstriert. Die MATLAB-Codes sind auf der Webseite des Verlages zum Download bereitgestellt, so dass der Leser seine eigenen Experimente mit den numerischen Verfahren durchführen kann.

Durch seinen didaktischen Aufbau und die zahlreichen anschaulichen Beispiele und Übungsaufgaben eignet sich dieses Buch hervorragend als vorlesungsbegleitende Lektüre und als Grundlage für ein erfolgreiches Selbststudium. Gleichzeitig kann es von Mathematikern, Naturwissenschaftlern und Ingenieuren als profundes Nachschlagewerk herangezogen werden.

Mit der 4. Auflage wurde das umfangreiche Standardwerk der Numerischen Mathematik so in zwei Bände aufgeteilt, dass diese relativ unabhängig voneinander gelesen werden können. An vielen Stellen wurde der Text überarbeitet und ergänzt. Das betrifft insbesondere diejenigen Abschnitte, die für Lehrerstudenten relevant sind sowie die Implementierung der numerischen Verfahren in der Programmiersprache MATLAB.

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This second volume focuses on applications in engineering, biomedical engineering, computational physics and computer science.

Dieses Buch hat sich seit der zweiten Auflage zu einem vielbeachteten Klassiker im deutschsprachigen Raum entwickelt. Wesentliche Konzepte der Numerik von Differentialgleichungen werden an einfacheren Problemen wie Drei-Term-Rekursionen oder numerische Quadratur dargestellt. Neu hinzu gekommen in der 5. Auflage sind insbesondere aktuelle Forschungsergebnisse zur effizienten Polynominterpolation, die auf Trefethen et al. zurückgehen.

The relaxation method has enjoyed an intensive development during many decades and this new edition of this comprehensive text reflects in particular the main achievements in the past 20 years. Moreover, many further improvements and extensions are included, both in the direction of optimal control and optimal design as well as in numerics and applications in materials science, along with an updated treatment of the abstract parts of the theory.

Numerical analysis deals with the development and analysis of algorithms for scientific computing, and is in itself a very important part of mathematics, which has become more and more prevalent across the mathematical spectrum. This book is an introduction to numerical methods for solving linear and nonlinear systems of equations as well as ordinary and partial differential equations, and for approximating curves, functions, and integrals.

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.

The volume covers wide-ranging topics from Theory: structure of finite fields, normal bases, polynomials, function fields, APN functions. Computation: algorithms and complexity, polynomial factorization, decomposition and irreducibility testing, sequences and functions. Applications: algebraic coding theory, cryptography, algebraic geometry over finite fields, finite incidence geometry, designs, combinatorics, quantum information science.

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science.

The organization of the material is presented as follows:

This introductory chapter I represents a theoretical analysis of the computational algorithms for a numerical solution of the basic equations in continuum mechanics. In this chapter, the general requirements for computational grids, discretization, and iterative methods for black-box software are examined. Finally, a concept of a two-grid algorithm for (de-)coupled solving multidimensional non-linear (initial-)boundary value problems in continuum mechanics (multiphysics simulation) in complex domains is presented.

Chapter II contains descriptions of the sequential Robust Multigrid Technique which is developed as a general-purpose solver in black-box codes. This chapter presents the main components of the Robust Multigrid Technique (RMT) used in the two-grid algorithm (Chapter I) to compute the auxiliary (structured) grid correction. This includes the generation of multigrid structures, computation of index mapping, and integral evaluation. Finite volume discretization on the multigrid structures will be explained by studying a 1D linear model problem. In addition, the algorithmic complexity of RMT and black-box optimization of the problem-dependent components of RMT are analysed.

Chapter III provides a description of parallel RMT. This chapter introduces parallel RMT-based algorithms for solving the boundary value problems and initial-boundary value problems in unified manner. Section 1 presents a comparative analysis of the parallel RMT and the sequential V-cycle. Sections 2 and 3 present a geometric and an algebraic parallelism of RMT, i.e. parallelization of the smoothing iterations on the coarse and the levels. A parallel multigrid cycle will be considered in Section 4. A parallel RMT for the time-dependent problems is given in Section 5. Finally, the basic properties of parallel RMT will be summarized in Section 6.

Theoretical aspects of the used algorithms for solving multidimensional problems are discussed in Chapters IV. This chapter contains the theoretical aspects of the algorithms used for the numerical solving of the resulting system of linear algebraic equations obtained from discrete multidimensional (initial-)boundary value problems.

This book on finite element-based computational methods for solving incompressible viscous fluid flow problems shows readers how to apply operator splitting techniques to decouple complicated computational fluid dynamics problems into a sequence of relatively simpler sub-problems at each time step, such as hemispherical cavity flow, cavity flow of an Oldroyd-B viscoelastic flow, and particle interaction in an Oldroyd-B type viscoelastic fluid. Efficient and robust numerical methods for solving those resulting simpler sub-problems are introduced and discussed. Interesting computational results are presented to show the capability of methodologies addressed in the book.

This book uses numerical analysis as the main tool to investigate methods in machine learning and neural networks. The efficiency of neural network representations for general functions and for polynomial functions is studied in detail, together with an original description of the Latin hypercube method and of the ADAM algorithm for training. Furthermore, unique features include the use of Tensorflow for implementation session, and the description of on going research about the construction of new optimized numerical schemes.

Die Mathematik ist in allen Bereichen der modernen Gesellschaft ein wichtiges Hilfsmittel, um bei der Lösung der vielfältigen Probleme mit zu wirken, insbesondere in Technik, Naturwissenschaften und Ökonomie. Die Mitwirkung mathematischer Methoden bei der Lösung praktischer Aufgaben in diesen Bereichen vollzieht sich in folgenden Schritten:

• Präzise Formulierung des Problems: die Aufgabenstellung muss klar erkennbar werden. Es ist kenntlich zu machen, welche Schwerpunkte zu setzen sind.
• Erarbeitung eines mathematischen Modells des Problems: aus der technischen, ökonomischen oder sonstigen Aufgabenstellung ist ein für die mathematische Behandlung geeignetes Modell zu entwickeln und einer mathematischen Behandlung zugänglich zu machen.
• Numerische Berechnung der Lösung: es sind geeignete Algorithmen und die erforderlichen Rechenhilfsmittel auszuwählen. Es sind Anforderungen an den numerischen Algorithmus bezüglich Rechenzeit und Rechengenauigkeit zu formulieren, sowie Abschätzungen der zu erwartenden Rechenfehler durchzuführen.
• Auswertung der erzielten Resultate: Es ist zu entscheiden, ob eine ausreichende Lösung des praktischen Problems erzielt worden ist oder ob eine Verbesserung des mathematischen Modells erfolgen muss.

Optimal feedback control arises in different areas such as aerospace engineering, chemical processing, resource economics, etc. In this context, the application of dynamic programming techniques leads to the solution of fully nonlinear Hamilton-Jacobi-Bellman equations. This book presents the state of the art in the numerical approximation of Hamilton-Jacobi-Bellman equations, including post-processing of Galerkin methods, high-order methods, boundary treatment in semi-Lagrangian schemes, reduced basis methods, comparison principles for viscosity solutions, max-plus methods, and the numerical approximation of Monge-Ampère equations. This book also features applications in the simulation of adaptive controllers and the control of nonlinear delay differential equations.

Contents
From a monotone probabilistic scheme to a probabilistic max-plus algorithm for solving Hamilton–Jacobi–Bellman equations
Improving policies for Hamilton–Jacobi–Bellman equations by postprocessing
Viability approach to simulation of an adaptive controller
Galerkin approximations for the optimal control of nonlinear delay differential equations
Efficient higher order time discretization schemes for Hamilton–Jacobi–Bellman equations based on diagonally implicit symplectic Runge–Kutta methods
Numerical solution of the simple Monge–Ampere equation with nonconvex Dirichlet data on nonconvex domains
On the notion of boundary conditions in comparison principles for viscosity solutions
Boundary mesh refinement for semi-Lagrangian schemes
A reduced basis method for the Hamilton–Jacobi–Bellman equation within the European Union Emission Trading Scheme

This monograph studies optimization problems for rigid punches in elastic media and for high-speed penetration of rigid strikers into deformed elastoplastic, concrete, and composite media using variational calculations, tools from functional analysis, and stochastic and min-max (guaranteed) optimization approaches with incomplete data. The book presents analytical and numerical results developed by the authors during the last ten years.

Contents
Part I: Quasi-statics of contact interaction
Optimal contact pressure and optimal punch shape
Influence of forces applied outside contact region
Shape optimization of punch moving with friction
Optimization in contact problems under incomplete data
Application of probabilistic data
Minimization of material wear
Multiobjective optimization of contact pressure, wear of material, and energy dissipation

Part II: High-speed penetration into deformable medium
Optimization of impactor shape
Strikers of nonaxisymmetric optimal shape
Multiobjective optimization of rigid shell
Optimization of truncated rotating strikers
Penetration into solid medium under strength constraints
Some problems of global multipurpose structural optimization
Analytical and numerical estimations of optimal parameters of layered slab
Striker shape optimization using condition of minimal ballistic limit velocity for layered slabs

Part III: Appendices
Nonadditive functionals and extremum conditions
Multivariant estimation of functionals and their sensitivity
Basic ideas of multicriteria optimization
Evolutionary algorithm of global optimization

This book presents a detailed description of a robust pseudomultigrid algorithm for solving (initial-)boundary value problems on structured grids in a black-box manner. To overcome the problem of robustness, the presented Robust Multigrid Technique (RMT) is based on the application of the essential multigrid principle in a single grid algorithm. It results in an extremely simple, very robust and highly parallel solver with close-to-optimal algorithmic complexity and the least number of problem-dependent components. Topics covered include an introduction to the mathematical principles of multigrid methods, a detailed description of RMT, results of convergence analysis and complexity, possible expansion on unstructured grids, numerical experiments and a brief description of multigrid software, parallel RMT and estimations of speed-up and efficiency of the parallel multigrid algorithms, and finally applications of RMT for the numerical solution of the incompressible Navier Stokes equations. Potential readers are graduate students and researchers working in applied and numerical mathematics as well as multigrid practitioners and software programmers.

Contents
Introduction to multigrid
Robust multigrid technique
Parallel multigrid methods
Applications of multigrid methods in computational fluid dynamics

Scientists and engineers are mainly using Richardson extrapolation as a computational tool for increasing the accuracy of various numerical algorithms for the treatment of systems of ordinary and partial differential equations and for improving the computational efficiency of the solution process by the automatic variation of the time-stepsizes. A third issue, the stability of the computations, is very often the most important one and, therefore, it is the major topic studied in all chapters of this book.
Clear explanations and many examples make this text an easy-to-follow handbook for applied mathematicians, physicists and engineers working with scientific models based on differential equations.

Contents
The basic properties of Richardson extrapolation
Richardson extrapolation for explicit Runge-Kutta methods
Linear multistep and predictor-corrector methods
Richardson extrapolation for some implicit methods
Richardson extrapolation for splitting techniques
Richardson extrapolation for advection problems
Richardson extrapolation for some other problems
General conclusions

This self-contained text is a step-by-step introduction and a complete overview of interval computation and result verification, a subject whose importance has steadily increased over the past many years. The author, an expert in the field, gently presents the theory of interval analysis through many examples and exercises, and guides the reader from the basics of the theory to current research topics in the mathematics of computation.

Contents

• Preliminaries
• Real intervals
• Interval vectors, interval matrices
• Expressions, P-contraction, ε-inflation
• Linear systems of equations
• Nonlinear systems of equations
• Eigenvalue problems
• Automatic differentiation
• Complex intervals

With a focus on the interplay between mathematics and applications of imaging, the first part covers topics from optimization, inverse problems and shape spaces to computer vision and computational anatomy. The second part is geared towards geometric control and related topics, including Riemannian geometry, celestial mechanics and quantum control.

Contents:
Part I
Second-order decomposition model for image processing: numerical experimentation
Optimizing spatial and tonal data for PDE-based inpainting
Image registration using phase・amplitude separation
Rotation invariance in exemplar-based image inpainting
Convective regularization for optical flow
A variational method for quantitative photoacoustic tomography with piecewise constant coefficients
On optical flow models for variational motion estimation
Bilevel approaches for learning of variational imaging models
Part II
Non-degenerate forms of the generalized Euler・Lagrange condition for state-constrained optimal control problems
The Purcell three-link swimmer: some geometric and numerical aspects related to periodic optimal controls
Controllability of Keplerian motion with low-thrust control systems
Higher variational equation techniques for the integrability of homogeneous potentials
Introduction to KAM theory with a view to celestial mechanics
Invariants of contact sub-pseudo-Riemannian structures and Einstein・Weyl geometry
Time-optimal control for a perturbed Brockett integrator
Twist maps and Arnold diffusion for diffeomorphisms
A Hamiltonian approach to sufficiency in optimal control with minimal regularity conditions: Part I
Index

By discussing topics such as shape representations, relaxation theory and optimal transport, trends and synergies of mathematical tools required for optimization of geometry and topology of shapes are explored. Furthermore, applications in science and engineering, including economics, social sciences, biology, physics and image processing are covered.

Contents
Part I

• Geometric issues in PDE problems related to the infinity Laplace operator
• Solution of free boundary problems in the presence of geometric uncertainties
• Distributed and boundary control problems for the semidiscrete Cahn–Hilliard/Navier–Stokes system with nonsmooth Ginzburg–Landau energies
• High-order topological expansions for Helmholtz problems in 2D
• On a new phase field model for the approximation of interfacial energies of multiphase systems
• Optimization of eigenvalues and eigenmodes by using the adjoint method
• Discrete varifolds and surface approximation

Part II

• Weak Monge–Ampere solutions of the semi-discrete optimal transportation problem
• Optimal transportation theory with repulsive costs
• Wardrop equilibria: long-term variant, degenerate anisotropic PDEs and numerical approximations
• On the Lagrangian branched transport model and the equivalence with its Eulerian formulation
• On some nonlinear evolution systems which are perturbations of Wasserstein gradient flows
• Pressureless Euler equations with maximal density constraint: a time-splitting scheme
• Convergence of a fully discrete variational scheme for a thin-film equatio
• Interpretation of finite volume discretization schemes for the Fokker–Planck equation as gradient flows for the discrete Wasserstein distance

All relevant implementation aspects of finite element methods are discussed in this book. The focus is on algorithms and data structures as well as on their concrete implementation. Theory is covered only as far as it gives insight into the construction of algorithms. In the exercises, a complete FE-solver for stationary 2D problems is implemented in Matlab/Octave.

Contents:
Finite Element Fundamentals
Grids and Finite Elements
Assembly
Solvers
Error Estimation
Mesh Refinement
Multigrid
Elastomechanics
Fluid Mechanics
Grid Data Structure
Function Reference

Nonlinear evolution equations are widely used to describe nonlinear phenomena in natural and social sciences. However, they are usually quite difficult to solve in most instances. This book introduces the finite difference methods for solving nonlinear evolution equations. The main numerical analysis tool is the energy method. This book covers the difference methods for the initial-boundary value problems of twelve nonlinear partial differential equations. They are Fisher equation, Burgers' equation, regularized long-wave equation, Korteweg-de Vries equation, Camassa-Holm equation, Schrödinger equation, Kuramoto-Tsuzuki equation, Zakharov equation, Ginzburg-Landau equation, Cahn-Hilliard equation, epitaxial growth model and phase field crystal model. This book is a monograph for the graduate students and science researchers majoring in computational mathematics and applied mathematics. It will be also useful to all researchers in related disciplines.

This monograph develops adaptive stochastic methods in computational mathematics. The authors discuss the basic ideas of the algorithms and ways to analyze their properties and efficiency. Methods of evaluation of multidimensional integrals and solutions of integral equations are illustrated by multiple examples from mechanics, theory of elasticity, heat conduction and fluid dynamics.

Contents

Part I: Evaluation of Integrals
Fundamentals of the Monte Carlo Method to Evaluate Definite Integrals
Sequential Monte Carlo Method and Adaptive Integration
Methods of Adaptive Integration Based on Piecewise Approximation
Methods of Adaptive Integration Based on Global Approximation
Numerical Experiments
Adaptive Importance Sampling Method Based on Piecewise Constant Approximation

Part II: Solution of Integral Equations
Semi-Statistical Method of Solving Integral Equations Numerically
Problem of Vibration Conductivity
Problem on Ideal-Fluid Flow Around an Airfoil
First Basic Problem of Elasticity Theory
Second Basic Problem of Elasticity Theory
Projectional and Statistical Method of Solving Integral Equations Numerically

This book is intended to provide a fast, interdisciplinary introduction to the basic results of p-adic analysis and its connections with mathematical physics and applications. The book revolves around three topics: (1) p-adic heat equations and ultradiffusion; (2) fundamental solutions and local zeta functions, Riesz kernels, and quadratic forms; (3) Sobolev-type spaces and pseudo-differential evolution equations. These topics are deeply connected with very relevant current research areas. The book includes numerous examples, exercises, and snapshots of several mathematical theories. This book arose from the need to quickly introduce mathematical audience the basic concepts and techniques to do research in p-adic analysis and its connections with mathematical physics and other areas. The book is addressed to a general mathematical audience, which includes computer scientists, theoretical physicists, and people interested in mathematical analysis, PDEs, etc.

Currently, nonstandard analysis is barely considered in university teaching. The author argues that nonstandard analysis is valuable not only for teaching, but also for understanding standard analysis and mathematics itself. An axiomatic approach wich pays attention to different language levels (for example, in the distinction between sums of ones and the natural numbers of the theory) leads naturally to a nonstandard theory. For motivation historical ideas of Leibniz can be taken up. The book contains an elaborated concept that follows this approach and is suitable, for example, as a basis for a lecture-supplementary course.

The monograph part presents all major approaches to nonstandard analysis and discusses logical, model-theoretic, and set-theoretic investigations to reveal possible mathematical reasons that may lead to reservations about nonstandard analysis. Also various foundational positions as well as ontological, epistemological, and application-related issues are addressed. It turns out that the one-sided preference for standard analysis is justified neither from a didactic, mathematical nor philosophical point of view.

Thus, the book is especially valuable for students and instructors of analysis who are also interested in the foundations of their subject.